package com.pjq.java.ai.langchain4j.config;

import com.pjq.java.ai.langchain4j.store.MongoChatMemoryStore;
import dev.langchain4j.data.document.Document;
import dev.langchain4j.data.document.loader.FileSystemDocumentLoader;
import dev.langchain4j.data.segment.TextSegment;
import dev.langchain4j.memory.chat.ChatMemoryProvider;
import dev.langchain4j.memory.chat.MessageWindowChatMemory;
import dev.langchain4j.rag.content.retriever.ContentRetriever;
import dev.langchain4j.rag.content.retriever.EmbeddingStoreContentRetriever;
import dev.langchain4j.store.embedding.EmbeddingStoreIngestor;
import dev.langchain4j.store.embedding.inmemory.InMemoryEmbeddingStore;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;



import java.io.IOException;
import java.util.Arrays;
import java.util.List;

@Configuration
public class XiaopengAgentConfig {
    @Autowired
    private MongoChatMemoryStore mongoChatMemoryStore;
    @Bean
    ChatMemoryProvider chatMemoryProviderXiaopeng() {
        return memoryId -> MessageWindowChatMemory.builder()
                .id(memoryId)
                .maxMessages(20)
                .chatMemoryStore(mongoChatMemoryStore)
                .build();
    }
    @Bean
    ContentRetriever contentRetrieverXiaopeng() throws IOException {
//使用FileSystemDocumentLoader读取指定目录下的知识库文档
//并使用默认的文档解析器对文档进行解析
        Document document1 = FileSystemDocumentLoader.loadDocument(
                "C:\\aPJQ\\ai-健身房助手\\java-ai-langchain4j\\src\\main\\resources\\knowlege\\武汉江城健身中心介绍.md");

        List<Document> documents = Arrays.asList(document1);

//使用内存向量存储
        InMemoryEmbeddingStore<TextSegment> embeddingStore = new InMemoryEmbeddingStore<>
                ();
//使用默认的文档分割器
        EmbeddingStoreIngestor.ingest(documents, embeddingStore);
//从嵌入存储（EmbeddingStore）里检索和查询内容相关的信息
        return EmbeddingStoreContentRetriever.from(embeddingStore);
    }


}
